> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Autonomous Voice Agent

> An autonomous (max_loops="auto") agent with terminal access that narrates its plan, tool use, and final summary in real time.

This example combines three Swarms features into one voice-driven agent:

* `max_loops="auto"` — the autonomous plan → execute → summary loop.
* `selected_tools="all"` — the agent picks up every available tool, including the built-in bash terminal.
* `StreamingTTSCallback` — every token from every loop phase is narrated in real time.

The result is an agent that talks through its plan, the commands it runs, and its final summary — useful for accessibility, demos, and ambient operations dashboards.

## Step 1: Install dependencies

```bash theme={null}
pip install -U swarms voice-agents
export ANTHROPIC_API_KEY=sk-ant-...   # this example uses Claude
export OPENAI_API_KEY=sk-...          # required for OpenAI TTS
```

## Step 2: Build the autonomous agent

```python theme={null}
from swarms import Agent

agent = Agent(
    agent_name="Terminal-Agent",
    agent_description="Agent that can plan tasks and run bash commands on the terminal",
    model_name="anthropic/claude-sonnet-4-5",
    dynamic_temperature_enabled=True,
    max_loops="auto",
    dynamic_context_window=True,
    selected_tools="all",
    top_p=None,
)
```

`max_loops="auto"` runs the agent through three phases — plan, execute, summary — looping until it decides the task is done. `selected_tools="all"` exposes every tool the autonomous looper has access to (bash, file ops, etc.).

## Step 3: Wire up the streaming TTS callback

```python theme={null}
from voice_agents import StreamingTTSCallback

# voice options: alloy, echo, fable, onyx, nova, shimmer
tts_callback = StreamingTTSCallback(voice="alloy", model="tts-1")
```

## Step 4: Run the agent

The TTS callback receives tokens from **every** phase of the autonomous loop — the planning step, each tool-call/synthesis turn, and the final summary.

```python theme={null}
if __name__ == "__main__":
    result = agent.run(
        task="Use the terminal to list the current directory, and see what files are in it.",
        streaming_callback=tts_callback,
    )

    # Flush so the last sentence is spoken
    tts_callback.flush()

    print(result)
```

## Full example

```python theme={null}
from swarms import Agent
from voice_agents import StreamingTTSCallback

agent = Agent(
    agent_name="Terminal-Agent",
    agent_description="Agent that can plan tasks and run bash commands on the terminal",
    model_name="anthropic/claude-sonnet-4-5",
    dynamic_temperature_enabled=True,
    max_loops="auto",
    dynamic_context_window=True,
    selected_tools="all",
    top_p=None,
)

tts_callback = StreamingTTSCallback(voice="alloy", model="tts-1")


if __name__ == "__main__":
    result = agent.run(
        task="Use the terminal to list the current directory, and see what files are in it.",
        streaming_callback=tts_callback,
    )

    tts_callback.flush()
    print(result)
```

<Warning>
  An autonomous agent with `selected_tools="all"` can execute bash commands. Run it inside a sandbox or scoped working directory if the task is untrusted.
</Warning>

<Note>
  Source: [examples/guides/voice\_agents/voice\_agents\_examples/run\_auto\_agent\_with\_speech.py](https://github.com/kyegomez/swarms/blob/master/examples/guides/voice_agents/voice_agents_examples/run_auto_agent_with_speech.py)
</Note>

## See also

* [Streaming Voice Agent](/examples/voice-agents/agent-with-streaming-speech) — same callback pattern with a single-loop agent.
* [Hierarchical Speech Swarm](/examples/voice-agents/hierarchical-speech-swarm) — multi-agent voice differentiation.